Health State Assessment via Knowledge Graph Attention Network
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Solution Overview
Problem
Existing equipment health assessment methods fail to effectively fuse data from different sources and integrate spatial and temporal features, leading to limitations in accurately evaluating the health state of complex equipment.
Innovation Solution
A health state assessment method based on a knowledge graph attention network is developed, which constructs a comprehensive health state knowledge graph by integrating component relationships, monitoring data, and prior information, using a graph attention network to extract feature information and transform the assessment into a node classification problem.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If data-driven methods are used for health state assessment, then accuracy is improved, but the model lacks clear physical explanation and is disturbed by noise and abnormal samples
Solution Approach 1:
The patent merges data-driven methods with knowledge-driven methods by constructing a knowledge graph that integrates equipment component relationships, monitoring data, and expert knowledge. This combination allows the model to leverage both the accuracy of data-driven approaches and the physical interpretability of knowledge-driven approaches, resolving the contradiction between assessment accuracy and physical explanation.
Solution Approach 2:
The patent introduces a knowledge graph as an intermediary between raw monitoring data and the health state assessment model. The knowledge graph structures domain knowledge and equipment relationships, serving as a mediator that guides the data-driven model with physical insights while maintaining accuracy, thus preventing the model from being overly influenced by noise and abnormal samples.
2Device complexity
If knowledge-driven methods are used for health state assessment, then time-space complexity is reduced and physical meaning is clear, but incomplete and fuzzy priori knowledge reduces model accuracy
Solution Approach 1:
The patent combines knowledge-driven methods with data-driven methods, where the knowledge graph provides the structural framework with clear physical meaning, while monitoring data and deep learning models enhance accuracy. This merger allows the system to maintain low time-space complexity and clear physical interpretation while overcoming the limitations of incomplete prior knowledge through data-driven refinement.
3Ease of manufacture
If existing assessment methods are used, then implementation is straightforward, but they fail to fuse data from different sources and integrate spatial and temporal features
Solution Approach 1:
The patent transforms the health state assessment from traditional single-dimension analysis to multi-dimensional analysis by constructing a knowledge graph that simultaneously captures spatial relationships (equipment component structures) and temporal dynamics (monitoring data evolution). This dimensional expansion enables comprehensive fusion of multi-source data while maintaining implementation feasibility through structured knowledge representation.
Data Source
AI summary
Disclosed in the present invention is a health state assessment method for equipment based on a knowledge graph attention network, includes: steps: 1) constructing a graph data model which can comprehensively reflect change of a health state of the equipment by deeply integrating association relationships of equipment components, monitoring data dependence relationships and priori information, etc. by means of a knowledge graph and by combining with domain priori knowledge; 2) extracting feature information of the health state knowledge graph by using a graph attention network, and obtaining a target node vector representation which accurately reflects the health state of the equipment by means of learning; and 3) making a health state representation vector of the equipment pass through a fully connected layer to obtain a health state classification prediction probability, and performing training to reducing a loss value relative to a true label, thereby obtaining a health state assessment result.